Research

Research Overview

Research Statement

I work in the area of machine and natural intelligence, following the statistical approach pioneered by Ulf Grenander, which we call "Pattern Theory". In this approach, thinking is modeled as statistical inference rather than logic and learning results from the accumulation of massive data from interactions with the world. My work concentrates on visual perception which seems to be more approachable than high-level thinking, more complex than auditory and tactile perception yet has been solved by three distinct classes of animals (cephalopods, birds and mammals) so it can't be that hard! The aspect of this research with which I have been most involved is the construction of probability models for the variables of vision: direct models of the raw images, the shapes of objects, the texture of their surfaces etc. A second question is how to sample and estimate with these models, e.g. compute conditional means and modes. Many approaches involve computing with one or more samples from the distribution which evolve deterministically or stochastically, smoothly or with jumps. Sampling from the distribution can be viewed as feedback, i.e. prior knowledge of high level structures guides the reconstruction of an image. A third set of questions concerns how such statistical estimation may be performed in cortex, in neural nets with feedback. A major open question here is whether the information being handled by neurons is contained only in their firing rates or in the precise timing and synchrony of their spikes in the full network. Another is how cortex handles ambiguity, the need for maintaining multiple hypotheses while waiting for more data.

Funded Research

You may contact an Administrative Assistant in the Division of Applied Mathematics for this information.